Electronics Work Bench ® And Pspice ® Computer Aided Design Systems As Educational Tools For Second And Fourth Year University Courses In Electronics
Bibliographic record
Abstract
The progress in development of comprehensive computer-aided design (CAD) tools for electronic systems is related to the efficiency of teaching both introductory and advanced courses in electronics at university level.Rapid development of graphical user interface (GUI) created opportunity to convert a personal computer into a virtual electronic development site and thus significantly simplify the applicability of these CAD systems in academic environment.The aim of this study was to compare both qualitatively and quantitatively the student utilization of two of the most popular CAD systems available on today's market, Electronics WorkBench and PSPICE.Twenty-nine second-year students and thirty four fourth-year students taking introductory and advanced courses in Electronics (Department of Electrical and Computer Engineering at the University of Calgary) volunteered to participate in the study, which examined the efficiency of their usage of the two systems in various assignments.The majority of junior students favored exposure to both systems, while the majority of senior students preferred Electronics WorkBench because of its quick learning curve and well-developed GUI environment.We conclude that visual CAD systems for electronic design are very well accepted by students.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".